ORIGINAL PAPER

Assessment of interpolated ERA-40 reanalysis temperatureand precipitation against observations of the Balkan Peninsula

Effie Kostopoulou & Christos Giannakopoulos &

Tom Holt & Philippe Le Sager

Received: 19 February 2009 /Accepted: 18 December 2009 /Published online: 21 January 2010# Springer-Verlag 2010

Abstract Due to their ready availability and temporal andspatial consistency, reanalysis data are widely used within theclimate community. Nevertheless, higher spatial resolutionsare often required and statistical interpolation techniques areapplied to increase the data resolution. This work aims toderive a set of high spatial resolution data through three-dimensional interpolation of daily temperature and precipita-tion. Thin plate spline interpolation has been chosen and usedto interpolate ERA-40 temperature and precipitation from acoarse grid (110 km) into a finer one of 1-km spatialresolution. The study evaluates the method by comparingthe simulated variables with available in situ meteorologicalmeasurements. The chosen stations are distributed over thestudy region and, most importantly, contain information froma range of altitudes. The results indicate that accounting for thetopography in the interpolation process improves the compar-isons, with the biggest improvements being evident in themost mountainous areas. The method is found to be better inestimating temperature than precipitation fields. Moreover,the method performs better for maximum temperature in highaltitudes and for minimum temperature in low altitudes.

1 Introduction

Modern climatological studies require long and compre-hensive climate records over large areas, making the use ofreanalysis data an essential component in climate applica-tions. The resolution of data is a critical issue, particularlyas far as regional climate studies are concerned, andtherefore, continuous improvement and refinement of thesedatasets are of high priority. The quality of such datadepends on several parameters but mainly on the assimila-tion method and the accuracy and density of the observa-tions network used. Quality may also vary from one climateelement to another, from one region to another, andbetween periods with different observational coverage.Principal activity in atmospheric reanalysis products iscurrently undertaken in three major climate diagnosticscenters: (a) the European Centre for Medium-RangeWeather Forecasts (ECMWF), (b) the United StatesNational Centers for Environmental Prediction, and (c) theJapan Meteorological Agency. The importance for im-provement and need for international coordination ofreanalysis are recognized in the Implementation Plan forthe Global Observing System for Climate (http://www.wmo.int/pages/prog/gcos/) in support of the United NationsFramework Convention on Climate Change.

During recent years, reanalysis data have been widelyused by scientists all over the world. Nevertheless, concernabout the accuracy of these data remains and validationstudies test their reliability using available measurements(Reid et al. 2001; Trenberth et al. 2001; Rusticucci andKousky 2002; Flocas et al. 2005; Christensen et al. 2007;Ma et al. 2008). The reanalysis products have particularlyfacilitated research in data-scarce regions where the stationdensity is low, and site measurements do not provide dataof sufficient accuracy for climate applications. Data-scarce

E. Kostopoulou (*) : C. GiannakopoulosInstitute for Environmental Research and Sustainable Development,National Observatory of Athens,I. Metaxa & V. Pavlou, Palaia Pendeli,15236 Athens, Greecee-mail: ekosto@meteo.noa.gr

T. HoltClimatic Research Unit, University of East Anglia,Norwich NR4 7TJ, UK

P. Le SagerHarvard School of Engineering and Applied Sciences,Harvard University,Cambridge, MA 02138, USA

Theor Appl Climatol (2010) 102:115–124DOI 10.1007/s00704-009-0249-z

areas include regions with weak infrastructure, wheremeteorological data are not available at sufficient spatialand temporal scales, areas at high latitudes, and mountainousregions. In many cases, however, there is a need to furtherincrease the spatial resolution of reanalyses and, hence,interpolation methods are applied to construct new datapoints within the range of a discrete set of known data points.

In this study, an interpolation scheme based on thin platespline (TPS) interpolation was developed to increase theoriginal spatial resolution of the reanalysis data fromapproximately 110 km (1° latitude/longitude) down to afiner grid of 1 km. To evaluate the proposed methodology,the variables derived from the interpolation of the ERA-40reanalysis were compared against station records from theBalkan region. The study considered several climateparameters, at two temporal resolutions, and was carriedout for a number of stations of varying topographicelevations. Moreover, tests were made with respect to theinterpolation skills by changing the input of data incorpo-rated into the interpolation process. Several runs wereundertaken for various rasters (grid surfaces) around theexamined locations. The spline models also incorporateddata from a digital elevation model (DEM) developedat the National Oceanic and Atmospheric Administration'sNational Geophysical Data Center (http://www.ngdc.noaa.gov/mgg/topo/globe.html), available at a 30-arcseconds(approximately 1 km) resolution. Moreover, the methodwas modified and enforced to admit the real altitude data atthe chosen sites in order to validate the DEM set.Additionally, two-dimensional (2D) interpolation was un-dertaken, without incorporating altitudinal information, todefine the importance of including altitude in the process.

2 Data and method

ERA-40 is a second-generation reanalysis dataset of globalatmospheric and surface conditions developed at theECMWF in collaboration with a number of partnerorganizations (Uppala et al. 2005). Many sources ofmeteorological observations were used, including landstations, ships and ocean buoys, radiosondes, pilot bal-loons, aircrafts, and satellites. The ERA-40 reanalysisproduct uses the T159L60 version of the IntegratedForecasting System to produce analyses every 6 h (Simmonsand Gibson 2000). Several of the problems experienced inERA-40 have been eliminated or significantly reduced; forinstance, a too-strong tropical oceanic precipitation that wasmarked from the early 1990s onwards. Although the ERA-40 still suffer from some weaknesses (e.g., from a too-strong Brewer–Dobson circulation in the stratosphere;Uppala et al. 2005), the data have been widely used inclimate studies (Cullather et al. 2000, Ansell et al. 2006;

Trenberth et al. 2005; Trenberth and Smith 2006; Bromwichet al. 2007; Raible et al. 2008). In addition, many climatemodel simulations are driven by ERA-40 reanalysis lateralboundary conditions and sea surface temperatures (Radu etal. 2008; Syed et al. 2009). A key advantage of the ERA-40product is that it is a homogeneous dataset which spans along time period (45 years).

Daily temperature and precipitation data from 21 stationsacross the Balkan Peninsula were used to evaluate theinterpolation results, over the period 1958–2000. The set ofstations used here were not part of the ERA-40 assimilationcycle. The stations were selected at various altitudes toassist the validation of the interpolated ERA-40 values in ahighly variable terrain. Figure 1 shows the locations of thestations used, while Table 1 lists them in alphabetical order.The first two columns next to the station name represent thegeographical coordinates of the station and the closest tothe station ERA-40 grid point. The next two columns showthe real altitude of the station and the altitude as providedby the 1-km gridded global DEM. It becomes evident fromTable 1 that the DEM adequately represents the groundsurface topography, although some deviations exist in fewcases, as in the station of Milos. The latter is situated at183 m above sea level on the island of Milos in centralAegean Sea, whereas DEM inaccurately locates it at meansea level. The analysis was performed on maximum andminimum temperature and precipitation using monthly anddaily data.

Spatial interpolation constitutes a common approach forcreating surface data from a sample of control points (i.e.,known points such as observations or gridded values). The

14oE 18oE 22oE 26oE 30oE 34oE

34oN

38oN

42oN

46oN

1

23

4

5

6

78

9

10

11

12

13

14

15

1617

18

19

2021

0

500

1000

1500

2000

2500

(metres)

Fig. 1 Topographic map of the study area showing the locations ofthe meteorological stations (squares) and the corresponding gridpoints (crosses) used. Each station is assigned a map index number(see Table 1)

116 E. Kostopoulou et al.

number and distribution of control points can highly affectthe accuracy of the interpolation. The spline surface istypically used to fit a piecewise polynomial function to aneighborhood of sample points. Spline interpolation wasinitially developed primarily in geophysical science (Wahbaand Wendelberger 1980; Wahba 1990), and its applicationto climate analysis was later implemented by Hutchinson(1991). It is a stochastic method that creates a surfacewhich passes through the control points and has the leastpossible change in slope at all points. In other words, TPSfit the control points with a minimum curvature surface. Inthe present study, a three-dimensional (3D) interpolationmethod of position and elevation was used based on theTPS interpolation as proposed by Hutchinson (1998).Hutchinson (1998) investigated the climatic dependenceon topography and showed that there is a small butsignificant elevation effect on the daily climate data. Hence,a method was developed to compute a trivariate functionwhich incorporates the covariable elevation into bivariateTPS. The method's key features are its robustness andoperational simplicity, and therefore, it is often employed tospatially interpolate climatic data (Zheng and Basher 1995;Price et al. 2000; Hong et al. 2005; McKenney et al. 2006;Tait et al. 2006).

The primary goal of this work was to develop aninterpolation tool for deriving high spatial resolutiontemperature and precipitation data, based on a 3D interpo-

lation scheme. Since topography is a factor that stronglyinfluences climatic variables, altitudinal data are incorpo-rated into the interpolation procedure as a third spatialcovariate along with the more customary x and y geograph-ical coordinates. ERA-40 temperature and precipitationfields were interpolated from 1° resolution (approximately100 km in the domain of study) to a high spatial resolution1-km grid. The interpolation function at altitude h, latitude8, and longitude θ is defined as:

F q;8; hð Þ ¼ c0 þ c1q þ c28þ c3h

þX

ciþ4d2i ln d2i

� � ð1Þwhere the distance is d2i ¼ q � qið Þ2 þ 8� 8ið Þ2 and θi, 8iare the coordinates of input points. The coefficients ciare determined using the input points qi;8i; hið Þ :F qi;8i; hið Þ ¼ TERA�40 and conditions of orthogonality. Inthe 2D case, the variable h is not taken into account. Toevaluate the method, several runs were undertaken employ-ing 2D interpolation, 3D interpolation using a DEM, and3D interpolation using the real altitudes. Interpolationexperiments were made for a number of rasters surroundingevery examined site. Given the coordinates of a referencesite, the closest ERA-40 grid point was detected and thisserved as the central point for the construction of a rastergrid surface. Subsequently, the spline function wasdetermined by a 3×3 raster grid size (i.e., a nine-point

Table 1 Sites information

Station name Station longitude/latitude Central grid longitude/latitude Altitude (m) DEM (m)

Alexandroupoli 25°55′ E/40°51′ N 26° E/41° N 3 0

Beograd 20°28′ E/44°48′ N 20° E/45° N 132 150

Calarasi 27°20′ E/44°12′ N 27° E/44° N 18 16

Dimitrovgrad 22°45′ E/43°01′ N 23° E/43° N 450 453

Edirne 26°34′ E/41°40′ N 27° E/42° N 51 18

Helliniko 23°44′ E/37°54′ N 24° E/38° N 15 54

Ioannina 20°49′ E/39°42′ N 21° E/40° N 483 476

Kozani 21°47′ E/40°18′ N 22° E/40° N 627 675

Ljubljana 14°31′ E/46°04′ N 15° E/46° N 299 294

Milos 24°27′ E/36°43′ N 24° E/37° N 183 0

Mytilene 26°36′ E/39°04′ N 27° E/39° N 5 0

Pristina 21°09′ E/42°39′ N 21° E/43° N 573 563

Sarajevo 18°23′ E/43°51′ N 18° E/44° N 577 517

Shkodra 19°32′ E/42°06′ N 20° E/42° N 43 35

Thessaloniki 22°58′ E/40°31′ N 23° E/41° N 4 11

Tg Jiu 23°16′ E/45°02′ N 23° E/45° N 203 186

Tirana 19°47′ E/41°20′ N 20° E/41° N 89 72

Tripoli 22°24′ E/37°32′ N 22° E/38° N 652 665

Turnu Magurele 24°53′ E/43°45′ N 25° E/44° N 31 28

Varfu Omul 25°27′ E/45°27′ N 25° E/45° N 2,504 2,313

Zagreb 15°58′ E/45°49′ N 16° E/46° N 157 148

Assessment of interpolated ERA-40 reanalysis 117

grid), by extracting all grids, which lie within a radius of1° around the specific central point (i.e., station). Furtherruns of the method were carried out over larger rasterareas of 25, 81, and 298 points (5×5, 9×9 and 17×17grid, respectively). The results of the interpolation runswere then assessed against the station observations, aswell as among themselves.

3 Validation of the interpolation method: temperature

Scatter plots are used to display the series of the estimatedagainst the observed temperatures at each station tovisualize the relation between the two sets. In addition,the coefficient of determination (R-square) was calculatedas a measure of the goodness of fit of the relationshipbetween the two datasets. The analysis of the raw dailytemperatures revealed high R-square scores (>0.9 in allcases), indicating that the data in the model did well atpredicting the observations. Nevertheless, the mean fieldmay have biased the results. Hence, anomalies from themonthly means were computed (i.e., the January averagewas extracted from all the January days, etc.) to remove theseasonal cycle signal. In all cases, the shape and directionof the scatter plots denote positive correlations between thedatasets. Estimated maximum and minimum temperatures(TX and TN, respectively) satisfactorily reproduce theobservations especially in low-altitude stations. The resultsfor TN and TX in nine representative stations, located atvarious altitudes, are shown in Fig. 2. Overall, TX revealedslightly better simulations than TN, which appeared over-estimated at some stations. The difference is possibly theeffect of a nonlinear relationship between TN and elevationduring periods of atmospheric inversions. At the top leftcorner in every scatter plot, the R-squared value is givenwhich reflects the degree of correlation between the twodatasets. For both variables, correlation coefficients >0.7were obtained between observed and estimated data. Allcorrelations were found significant at the 0.05 level. It isworth noting that the R-square values appear lower in sitesof high elevation or in cases that the DEM misrepresentsthe altitude of the station such as in Helliniko station. Thesefindings highlight the importance of including a componentof altitude in interpolation or downscaling techniques.

A principal aim of this study is to define the benefit ofincorporating altitude in the interpolation process in orderto produce better climate estimates in regions that observa-tional data are unavailable. Therefore, the spline methodwas employed both with and without the parameter ofaltitude. The graphs in Fig. 3 show the differences betweenestimated temperatures produced with and without thetopography factor. The contour plots are used to explorethe possible weaknesses and strengths of the 3D method

(with topography) to estimate better than the 2D method(without topography) the daily temperature extremes (TN,TX) within the year over the 43 years of study. Thecomparisons for TX (Fig. 3a, b) revealed discrepanciesbetween the experiment results, which correspond topositive (negative) differences in winter (summer). Itbecame evident that, when the effect of altitude is nottaken into account, TX is underestimated in winter andoverestimated in summer. TN was found more complicatedto be reproduced, as differences have been varied betweenexperiments with and without elevation (Fig. 3c). Overall,the improvement was greater at high-altitude stations forthe winter season. In addition, the method was thenmodified to enable it to use the real altitude instead of theDEM input. There has been no major improvement in theresults (not shown), indicating that the DEM is a goodrepresentation of the real altitudes.

As mentioned above, several runs of the interpolationprocess were undertaken for various raster sizes toinvestigate whether the method is likely to improve byincreasing the size of the grid nodes inserted in the process.Hence, experiments were carried out over raster surfaces ofregularly spaced 3×3 points (one grid spacing away fromthe central point in every direction, RES1), 5×5 (two gridsaway from the central point, RES2), 9×9 (four grids awayfrom the central point, RES4), and 17×17 (eight grids awayfrom the central point, RES8) grid points. Figure 4 showsthe differences in estimated mean monthly temperaturesamong different rasters. Improvements in the results werefound when shifting from a 3×3 (RES1) to a 5×5 raster(RES2), yet the use of a much wider raster does not seem toprovide significant additional improvement. Moreover, theuse of a large-sized raster tends to provide smoothinterpolated fields, as such approach tends to lose distin-guishable features of the local topography, which has aprofound impact in the temperature and precipitationpatterns of a region. In this study, the evaluation of theinterpolation method is based on the results using a rastersize of 5×5 grids.

To further evaluate the performance of the interpolationscheme in estimating single-site temperature and precipita-tion data, several statistical quantities were considered andshown in Table 2. The first four columns correspond tominimum temperature and present the average difference ofthe means, difference in the standard deviations, thecorrelation coefficient (r), and the root mean square error(RMSE) between the examined datasets. The next fourcolumns show the results for maximum temperature and thelast four columns the results for the precipitation data. Asillustrated in Table 2, differences in mean TN rangebetween −2.6°C and 2.6°C with the exception of a largedifference (6.87°C) found in Varfu Omul which is locatedat a very high altitude (2,504 m). Another large difference

118 E. Kostopoulou et al.

is spotted in Tirana, which cannot be physically explainedand thus raises questions about the quality of the stationdata. Regarding TX, differences are mainly negative,indicating an underestimation of this variable, which inmost cases is <2°C. In both TN and TX, small differencesare found between the standard deviations of the compareddatasets, which means that the interpolated data represent

well the variability of the observational dataset. ThePearson product–moment correlation coefficient betweenthe TN and TX raw scores reveal high correlations (>0.95)among the two sets. In addition, RMSE between estimatedand observed values were computed as a measure of theaccuracy of the method. Table 2 presents RMSE magnitudesin the range of 1.3 to 3.3 for TN and slightly larger for TX.

-20

-10

0

10

20TN Alexandroupoli (3m)

Rsqrt = 0.75

TN Mytilene (5m)

Rsqrt = 0.83

TN Helliniko (15m)

Rsqrt = 0.65

-20

-10

0

10

20TN Edirne (51m)

Rsqrt = 0.75

TN Beograd (132m)

Rsqrt = 0.85

TN Zagreb (157m)

Rsqrt = 0.81

-20 -10 0 10 20 20-20

-10

0

10

20TN Ljubljana (299m)

Rsqrt = 0.82

-20 -10 0 10 20 20

TN Dimitrovgrad (450m)

Rsqrt = 0.77

-20 -10 0 10 20 20

TN Kozani (627m)

Rsqrt = 0.70

-20

-10

0

10

20TX Alexandroupoli (3m)

Rsqrt = 0.79

TX Mytilene (5m)

Rsqrt = 0.83

TX Helliniko (15m)

Rsqrt = 0.71

-20

-10

0

10

20TX Edirne (51m)

Rsqrt = 0.86

TX Beograd (132m)

Rsqrt = 0.92

TX Zagreb (157m)

Rsqrt = 0.90

-20 -10 0 10 20 20-20

-10

0

10

20TX Ljubljana (299m)

Rsqrt = 0.86

-20 -10 0 10 20 20

TX Dimitrovgrad (450m)

Rsqrt = 0.89

-20 -10 0 10 20 20

TX Kozani (627m)

Rsqrt = 0.85

Fig. 2 Scatter plots between theestimated and observed dailyminimum and maximumtemperature anomalies for theperiod 1958–2000

Assessment of interpolated ERA-40 reanalysis 119

Comparison of the RMSE scores suggests that TX is betterestimated in higher altitudes (e.g., Dimitrovgrad, Ioannina,Kozani, Ljubljana), whereas TN in lower altitudes (e.g.,Helliniko, Mytilene, Thessaloniki). The results for precipita-tion will be discussed in the next section.

Apart from daily records, monthly means were calculatedand correlations between the interpolated and observationalmean monthly data were computed for every station over theperiod of study (Fig. 5, top panels) to examine the seasonalcycle representation. Regarding TX, stations of low altitudesuch as Alexandroupoli, Thessaloniki, and Helliniko pre-sented small correlation coefficients, in the range of 0.7 to0.75, during the warm period of the year. In particular, theHelliniko station showed coefficients of 0.60 and 0.64 inJuly and August, respectively. Stations located at altitudesbetween 200 and 500 m revealed the higher correlations (r>0.90) especially in summer months. During winter, thecorrelations get lower, and the lowest value is observed inJanuary for the Ioannina station (r=0.71). As far as TNis concerned, lower correlation coefficients revealed for

summer in several stations at various elevations. The lowestcoefficients are around 0.56 to 0.63 and are found in Julyand August in the stations of Edirne, Ioannina, and Tripoli.Monthly TN seems to be better estimated in winter atstations located between 100 and 300 m altitudes. Inaddition, the contour difference (estimated−observed) plotsare shown in the bottom panels of Fig. 5, which indicate ageneral underestimation of TX during summer, whereas themethod tends to underestimate (overestimate) TN in low(high)-altitude regions. The greater underestimation of TX isdepicted in Ljubljana and Tg Jiu (approximately −3°C),while the Shkodra station reveals differences in the order of5°C, but yet again, the reliability of these data is questioned.Regarding TN, large underestimation (approximately −4°C)is seen at Turnu Magurele station and overestimation(approximately +4°C) in Ioannina. Additionally, it shouldbe noted that the weakest results are obtained for the stationof Varfu Omul (altitude 2,504 m), indicating that the methodfails in reproducing accurate estimated climate variables insites located over very rugged terrain.

4 Validation of the interpolation method: precipitation

To further examine the usefulness of the method, werepeated the analysis with rainfall records available from17 out of the 21 meteorological stations. The estimatedprecipitation results seemed to improve when the elevationfactor was included in the interpolation process. Improve-ments were bigger for summer (winter) in continental(coastal) sites. However, the method did not prove to be aseffective for precipitation as for temperature. In general, thecorrelation coefficients between station and estimated datarange from 0.52 to 0.62. Daily correlations of pairwisecomparisons for the wet season (October to May) in threeGreek stations are shown in Fig. 6. Results indicate weakand temporally ill-defined correlation coefficients in theseplots, as well as in those for the remaining sites (notshown). Nevertheless, in many stations (such as inThessaloniki and Tripoli), the coefficients of correlationwere found slightly higher in the cold period of the year(NDJF). It is worth to note that the highest precipitationtotals are observed during this time (Xoplaki et al. 2000),which are associated with the dominance of large-scaleatmospheric circulation patterns (Kostopoulou and Jones2007). Summer has almost no precipitation in this region,except for the precipitation which is mainly of convectiveorigin (Argiriou and Lykoudis 2006).

The use of different rasters did not improve theperformance of the method. Results obtained by differentrasters were quite similar and this was reflected in the highdegree of correlation (r=0.99) among different datasetsproduced by the various runs. The 43-year mean intra-

(a) TX Helliniko (15m)

100 200 300Day

1960

1970

1980

1990

2000

Yea

rs

(b) TX Tripoli (652m)

100 200 300Day

1960

1970

1980

1990

2000

Yea

rs

(c) TN Pristina (573m)

100 200 300Day

1960

1970

1980

1990

2000

Yea

rs

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Fig. 3 a–c Mean daily differences between temperatures interpolatedwith and without the elevation parameter. The x-axis corresponds tothe day of the year, while the y-axis presents the 43 years of study

120 E. Kostopoulou et al.

Table 2 Statistical indicators for the performance of TPS interpolation method in the estimation of temperature and precipitation data

TN TX RR

Diff mean Diff SD r RMSE Diff mean Diff SD r RMSE Diff mean Diff SD r RMSE

Alexandroupoli 2.60 0.45 0.96 3.29 −0.47 0.09 0.98 1.86 −0.26 −1.99 0.52 4.79

Beograd −0.02 0.13 0.98 1.53 −1.57 −0.13 0.99 2.14 −0.60 −2.30 0.34 4.82

Calarasi −1.70 −1.11 0.96 3.29 −1.97 −1.10 0.98 2.98 −0.05 −1.46 0.54 3.72

Dimitrovgrad 2.64 0.51 0.96 3.36 −1.69 −0.28 0.98 2.42 −0.26 −1.47 0.33 4.64

Edirne 1.68 0.14 0.96 2.57 −3.20 −1.54 0.98 3.95 −0.19 −1.82 0.34 4.98

Helliniko 0.62 −0.56 0.98 1.51 −2.53 −0.97 0.98 3.07 0.17 −0.81 0.58 3.74

Ioannina 2.21 0.98 0.95 3.28 −1.34 0.29 0.98 2.37 −1.22 −3.56 0.62 6.12

Kozani 1.43 0.71 0.96 2.57 −0.65 −0.05 0.98 1.92 0.14 −0.73 0.52 3.99

Ljubljana 0.88 0.29 0.98 1.89 −1.71 −0.69 0.98 2.45 −1.61 −4.38 0.38 8.81

Milos 1.98 −0.21 0.98 2.23 −0.48 −1.21 0.96 2.07 −0.15 −1.83 0.58 3.88

Mytilene 0.11 0.25 0.98 1.29 −1.45 −0.18 0.98 2.01 −0.42 −3.09 0.64 5.23

Pristina 1.63 0.25 0.97 2.56 −1.73 −0.44 0.98 2.60

Sarajevo 0.97 0.64 0.97 2.11 −1.36 −0.64 0.98 2.49 −0.29 −1.68 0.35 6.13

Shkondra −1.23 −0.08 0.93 2.86 −4.32 −1.13 0.88 6.34

TgJiu −2.61 −0.68 0.95 3.81 −2.60 −0.24 0.98 3.39 −0.53 −3.07 0.53 5.13

Thessaloniki −0.84 0.38 0.97 1.97 −2.82 0.47 0.98 3.41 0.18 −13.30 0.15 16.35

Tripoli −0.49 0.53 0.95 2.27 −4.26 0.74 0.97 4.79 −0.78 −2.91 0.62 5.15

Tirana 6.66 0.02 0.92 7.09 0.57 −1.40 0.97 2.37

Turnu Magurele −2.61 −1.30 0.96 3.91 −1.77 −0.99 0.98 2.77 −0.03 −1.25 0.50 4.07

Varfu Omul 6.87 2.24 0.83 8.72 10.62 3.05 0.86 11.98

Zagreb −1.44 0.47 0.98 2.25 −1.30 0.18 0.99 1.96 −0.57 −9.98 0.15 14.40

Fig. 4 Mean monthlydifferences between dataproduced by different resolutioninterpolating processes (RES13×3, RES2 5×5, RES4 9×9,and RES8 17×17 grid points)

Assessment of interpolated ERA-40 reanalysis 121

annual cycle showed an adequate reproduction of theprecipitation cycle for each site, although underestimatedmonthly rainfall totals were found mainly in high-altitudesites such as Ioannina and Tripoli (Fig. 7). In general, themethod used did not provide high confidence in interpolatingprecipitation in this region where orography is an importantfactor. The last four columns of Table 2 provide thestatistical indicators used to evaluate the interpolationmethod. The average precipitation differences in the meanand standard deviation are small; however, these quantitiesare often not so representative for precipitation. Moresignificant results are obtained from the two followingstatistics which reveal modest correlations (r∼0.5) amongthe two datasets and RMSE ranging from 3 to 8 mm. Theoverall message is that interpolation including the topog-

raphy factor provides an acceptable approach for produc-ing precipitation data in regions without meteorologicalobservations. However, climate research results based onsuch data should be interpreted with caution, as this paperconsiders that the suggested method requires furtherdevelopment.

5 Conclusions

In regions of complex relief and scarce meteorologicalinformation, the use of reanalysis products becomes animportant tool in climate research. In this research, dailytemperature and precipitation from ERA-40 reanalysis datawere interpolated from a 110-km (coarse) to a 1-km (fine)

TX CC

J F M A M J J A S O N DAlexandroupoli (3)

Thessaloniki (4)Mytilene (5)

Helliniko (15)Calarasi (18)

Turnu (31)Shkodra (43)

Edirne (51)Tirana (89)

Beograd (132)Zagreb (157)

Mylos (183)TgJiu (203)

Ljubljana (299)Dimitrovgrad (450)

Ioannina (483)Pristina (573)

Sarajevo (577)Kozani (627)Tripoli (652)Varfu (2504)

TN CC

J F M A M J J A S O N D

0.0 0.2 0.3 0.5 0.7 0.8 1.0

TX DIFF

J F M A M J J A S O N DAlexandroupoli (3)

Thessaloniki (4)Mytilene (5)

Helliniko (15)Calarasi (18)

Turnu (31)Shkodra (43)

Edirne (51)Tirana (89)

Beograd (132)Zagreb (157)

Mylos (183)TgJiu (203)

Ljubljana (299)Dimitrovgrad (450)

Ioannina (483)Pristina (573)

Sarajevo (577)Kozani (627)Tripoli (652)Varfu (2504)

TN DIFF

J F M A M J J A S O N D

-6.0 -4.0 -2.0 0.0 2.0 4.0 6.0

Fig. 5 Mean monthly correla-tions (CC) and differences(DIFF) between estimated andobserved temperatures forevery examined station overthe 43-year period

122 E. Kostopoulou et al.

resolution using TPS interpolation. This interpolationproduces spatially coherent data, which many methods donot, and is suggested to be used for studies of spatiallyvarying climate data. This study attempted a site-specificevaluation of the method. The proposed scheme usedinformation from a DEM and incorporated the elevationfactor in the process aiming to improve the interpolationresults. Several runs were carried out either changing thenumber of input grids, excluding the elevation parameter, orenabling the model to use specific altitude data instead oftracing data from DEM. Station data were used to assess thequality of the interpolated data and the findings of the studyare summarized as follows:

The proposed TPS interpolation scheme performedreasonably well in reproducing temperature data. Highpositive correlations were found between the estimated andobserved daily data. The accuracy of the method improvesby extending the grid surrounding the study area. Theresults improved from a raster of nine points to another of25 points. Nevertheless, the use of a very large grid is notnecessary as the improvement from a raster of 25 points toone of 289 points is negligible. Comparisons betweenestimated values derived with or without the elevationfactor taken into account have shown that the inclusion ofelevation information improves the results especially athigh-altitude sites. For instance, the employment of themethod without including the topography factor showedunderestimation (overestimation) of winter (summer) TX.The utilized DEM proved equivalent to the real altitudinalvalues in most cases.

In general, the interpolation scheme revealed better skillin simulating TX than TN. Both TN and TX were better

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Fig. 7 Mean monthly precipita-tion amounts as observed atfour stations and estimated bythe TPS method using both realand modeled altitude data

Assessment of interpolated ERA-40 reanalysis 123

reproduced in low altitudes, while comparing the perfor-mance of the method regarding the two temperaturevariables, the RMSE results showed better estimations formaximum temperature at high altitudes (in low altitudes,TX is underestimated), whereas minimum temperature isbetter estimated at low altitudes (in high altitudes, TN isoverestimated).

With respect to seasonal temperatures, the methodestimated better winter TX in low altitudes. Low correla-tions were obtained between estimated and observedsummer temperatures especially at low elevated regions,while summer TX was better represented in stations locatedbetween 200 and 500 m height. Summer TN is not well-reproduced, while the best estimations were found forwinter TN at 100–200 m height.

In contrast, the model exhibited lower skill in reproduc-ing precipitation. Precipitation estimates tend to improvewhen the elevation component is included in the analysis,with bigger improvements seen for summer precipitation incontinental and winter precipitation in coastal stations. Themethod succeeded to represent the seasonal cycle ofprecipitation; however, an overall underestimation of theprecipitation amounts appeared in high elevation sites.

Acknowledgements We would like to express our gratitude to theECMWF, whose ERA-40 data were used in this study. We would alsolike to thank all the meteorological institutions that provided thestation data. This study has been supported by the EuropeanCommission DeSurvey project (EU contract number: IP-003950).

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